{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0\n",
      "0    4.98\n",
      "1    9.14\n",
      "2    4.03\n",
      "3    2.94\n",
      "4    5.33\n",
      "..    ...\n",
      "501  9.67\n",
      "502  9.08\n",
      "503  5.64\n",
      "504  6.48\n",
      "505  7.88\n",
      "\n",
      "[506 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LSTAT  MEDV\n",
       "0   4.98  24.0\n",
       "1   9.14  21.6\n",
       "2   4.03  34.7\n",
       "3   2.94  33.4\n",
       "4   5.33  36.2"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "boston = datasets.load_boston() # Load Boston Dataset\n",
    "\n",
    "df = pd.DataFrame(boston.data[:, 12]) # Create DataFrame using only the LSATfeature\n",
    "\n",
    "print(df)\n",
    "\n",
    "df.columns = ['LSTAT']\n",
    "\n",
    "df['MEDV'] = boston.target # Create new column with the targetMEDV\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor # Import decision tree regression model\n",
    "\n",
    "X = df[['LSTAT']].values # Assign matrix X\n",
    "# print(X)\n",
    "\n",
    "y = df['MEDV'].values # Assign vector y\n",
    "\n",
    "sort_idx = X.flatten().argsort() # Sort X and y by ascendingvalues of X\n",
    "\n",
    "\n",
    "X = X[sort_idx]\n",
    "#print(X)\n",
    "print(type(X))\n",
    "\n",
    "y = y[sort_idx]\n",
    "\n",
    "tree = DecisionTreeRegressor(criterion='mse', # Initialize and fit regressor\n",
    "\n",
    "max_depth=3)\n",
    "# import pdb\n",
    "# pdb.set_trace()\n",
    "\n",
    "tree.fit(X, y)\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt \n",
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "plt.scatter(X, y, c='steelblue', # Plot actual target againstfeatures\n",
    "\n",
    "edgecolor='white',s=70)\n",
    "\n",
    "plt.plot(X, tree.predict(X), # Plot predicted targetagainst features\n",
    "\n",
    "color='black', lw=2)\n",
    "\n",
    "plt.xlabel('% lower status of the population [LSTAT]')\n",
    "\n",
    "plt.ylabel('Price in $1000s [MEDV]')\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
